grad = GradientAscent(gc)
grad.cid2pmap_dict = deepcopy(cid2pmap)
grad.load_nist_data(nist, skip_missing_reactions=True)
res_file = open('../res/evaluation_report.csv', 'w')
csv_results = csv.writer(res_file)
csv_results.writerow(["N", "dG0_obs", "dG0_est", "reaction", "pH", "I", "T", "evaluation"])
N = len(grad.data)
for n in range(n_begin, N):
(sparse_reaction, pH, I, T, evaluation, dG0_obs) = grad.data[n]
n_measurements = min([nist.cid2count[cid] for cid in sparse_reaction.keys()])
reaction_str = gc.kegg().sparse_reaction_to_string(sparse_reaction, cids=True)
dG0_est = grad.reaction_to_dG0(sparse_reaction, pH, I, T)
csv_results.writerow([n, dG0_obs, dG0_est, reaction_str, pH, I, T, evaluation, n_measurements])
res_file.flush()
################################################################################
if (len(sys.argv) > 1):
n_begin = int(sys.argv[1])
else:
n_begin = 0
gc = GroupContribution(sqlite_name="gibbs.sqlite", html_name="dG0_test")
gc.init()
nist = Nist(gc.kegg())
alberty = Alberty()
sensitivity_analysis_for_gradient_ascent(gc, nist, alberty.cid2pmap_dict, max_i=250, n_begin=n_begin)
#evaluate(gc, nist, alberty.cid2pmap_dict)
for n in range(n_begin, N):
(sparse_reaction, pH, I, T, evaluation, dG0_obs) = grad.data[n]
n_measurements = min(
[nist.cid2count[cid] for cid in sparse_reaction.keys()])
reaction_str = gc.kegg().sparse_reaction_to_string(sparse_reaction,
cids=True)
dG0_est = grad.reaction_to_dG0(sparse_reaction, pH, I, T)
csv_results.writerow([
n, dG0_obs, dG0_est, reaction_str, pH, I, T, evaluation,
n_measurements
])
res_file.flush()
################################################################################
if (len(sys.argv) > 1):
n_begin = int(sys.argv[1])
else:
n_begin = 0
gc = GroupContribution(sqlite_name="gibbs.sqlite", html_name="dG0_test")
gc.init()
nist = Nist(gc.kegg())
alberty = Alberty()
sensitivity_analysis_for_gradient_ascent(gc,
nist,
alberty.cid2pmap_dict,
max_i=250,
n_begin=n_begin)
#evaluate(gc, nist, alberty.cid2pmap_dict)
